This is CNN based number classification on the Kannada mnist data set

I choosed to build it with keras API (Tensorflow backend) which is very intuitive. Firstly, I will prepare the data (handwritten digits images) then i will focus on the CNN modeling and evaluation.

Repository for Kaggle Kannada MNIST Digit Recognition Challenge 2019

CNN Model for Kannada Hand-written Digit Recognition

Classification & Prediction of Images of Handwritten Digits in the Kannada Language.

This project aims to classify handwritten Kannada digits using multiple layers of algorithms.

Simple feedforward NN for Kannada Digits.

Repository containing codes made for different Kaggle's Competitions. Competitions in this repository: Digit Recognizer, Titanic and Kannada MNIST.

BNN trained on Kannada MNIST

capsule network for handwritten digit recognition

Handwritten Numeral Recognition using Convolutional Neural Networks

Kannada OCR (Optical Character Recognition) with ML (Machine Learning) classification algorithm involves training a machine learning model to recognize and classify Kannada characters from scanned or digital images.

Using a Convolutional Neural Network (CNN) to identify the Kannada numerical digits. Tensorflow (Keras) is used to create, train and load the neural network model. CustomTKinter/TKinter are used to provide the GUI and OpenCV is used to read input form the GUI.

A calculator that uses handwritten Kannada digits and operators to calculate the result, using contour detection and CNN model predictions. Made using PyTorch, OpenCV, PIL and CustomTkinter.